Unlike previous research on graph embedding, in this work we focus on the challenge of enforcing fairnes or invariance constraints on the learned representations.
Recent work on fairness in machine learning, including work on fairness in collaborative filtering, involves making predictions that are balanced or invariant with respect to certain sensitive variables (e.g., age or gender) [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
Formally, in the standard “fair classification” setting we consider a data point \(\mathbf {x} \in \mathbb {R}^n\) , its class label \(y \in \mathcal {Y}\) , and a binary sensitive attribute \(a \in \lbrace 0,1\rbrace \)  (e.g., indicating gender).
The high-level goal is then to train a model to predict \(y\)  from \(\mathbf {x}\) , while making this prediction invariant or fair with respect to \(a\)  [4]}.
There are many specific definitions of fairness, such as whether fairness refers to parity or satisfying certain preferences (see [2]} for a detailed discussion).
In the context of fair machine learning, our core contribution is motivating, implementing, and evaluating an approach to enforce fairness within the context of graph embeddings.
There are a number of complications introduced by this setting—for instance, rather than one classification task, we instead have thousands or even millions of interdependent edge relationships.
